Author Correction: Kernel approximation using analogue in-memory computing
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Error-driven upregulation of memory representations
Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood. To address this gap, participants performed a feedback-based association learning task and a 1-back localizer task. Increased hemodynamic responses in posterior medial frontal cortex were found for internal and external origins of memory error evidence, and during post-error encoding success as quantified by subsequent recall of face-associated memories. A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. Representation strength was higher during failed recall and increased during encoding when subsequent recall succeeded. These data enhance our understanding of the neurophysiological mechanisms of adaptive learning by linking the need for learning with increased processing of the relevant stimulus category.
Psychological booster shots targeting memory increase long-term resistance against misinformation
An increasing number of real-world interventions aim to preemptively protect or inoculate people against misinformation. Inoculation research has demonstrated positive effects on misinformation resilience when measured immediately after treatment via messages, games, or videos. However, very little is currently known about their long-term effectiveness and the mechanisms by which such treatment effects decay over time. We start by proposing three possible models on the mechanisms driving resistance to misinformation. We then report five pre-registered longitudinal experiments (Ntotal = 11,759) that investigate the effectiveness of psychological inoculation interventions over time as well as their underlying mechanisms. We find that text-based and video-based inoculation interventions can remain effective for one month—whereas game-based interventions appear to decay more rapidly—and that memory-enhancing booster interventions can enhance the diminishing effects of counter-misinformation interventions. Finally, we propose an integrated memory-motivation model, concluding that misinformation researchers would benefit from integrating knowledge from the cognitive science of memory to design better psychological interventions that can counter misinformation durably over time and at-scale.
Separate orexigenic hippocampal ensembles shape dietary choice by enhancing contextual memory and motivation
The hippocampus (HPC) has emerged as a critical player in the control of food intake, beyond its well-known role in memory. While previous studies have primarily associated the HPC with food intake inhibition, recent research suggests a role in appetitive processes. Here we identified spatially distinct neuronal populations within the dorsal HPC (dHPC) that respond to either fats or sugars, potent natural reinforcers that contribute to obesity development. Using activity-dependent genetic capture of nutrient-responsive dHPC neurons, we demonstrate a causal role of both populations in promoting nutrient-specific intake through different mechanisms. Sugar-responsive neurons encoded spatial memory for sugar location, whereas fat-responsive neurons selectively enhanced the preference and motivation for fat intake. Importantly, stimulation of either nutrient-responsive dHPC neurons increased food intake, while ablation differentially impacted obesogenic diet consumption and prevented diet-induced weight gain. Collectively, these findings uncover previously unknown orexigenic circuits underlying macronutrient-specific consumption and provide a foundation for developing potential obesity treatments.
Integrating molecular photoswitch memory with nanoscale optoelectronics for neuromorphic computing
Photonic solutions are potentially highly competitive for energy-efficient neuromorphic computing. However, a combination of specialized nanostructures is needed to implement all neuro-biological functionality. Here, we show that donor-acceptor Stenhouse adduct dyes integrated with III-V semiconductor nano-optoelectronics have combined excellent functionality for bio-inspired neural networks. The dye acts as synaptic weights in the optical interconnects, while the nano-optoelectronics provide neuron reception, interpretation and emission of light signals. These dyes can reversibly switch from absorbing to non-absorbing states, using specific wavelength ranges. Together, they show robust and predictable switching, low energy thermal reset and a memory dynamic range from days to sub-seconds that allows both short- and long-term memory operation at natural timescales. Furthermore, as the dyes do not need electrical connections, on-chip integration is simple. We illustrate the functionality using individual nanowire photodiodes as well as arrays. Based on the experimental performance metrics, our on-chip solution is capable of operating an anatomically validated model of the insect brain navigation complex.
Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
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